• International Journal of Contents
• /
• v.4 no.1
• /
• pp.20-25
• /
• 2008

The development of communication technology and the spread of computers and internet have affected to the field of education. In this paper, we design a learning process with LAMS to make the new education environment which is required in an information age. We made the learning environment with LAMS which develops the learner's algorithmic thinking faculty on some sorting algorithm, especially such as selection sort, bubble sort and insertion sort algorithm. In addition, we analyse the effectiveness of the learning environment. The designed contents were applied to elementary school students' learning and a questionnaire survey was conducted after a learning course. The research of the questionnaire shows that the learning system using LAMS motivates a learner for learning and provides a convenient learning environment.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.49-62
• /
• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.159-168
• /
• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.49-56
• /
• 2023

Computer architecture serves as a link between application requirements and underlying technology capabilities such as technical, mathematical, medical, and business applications' computational and storage demands are constantly increasing. Machine learning these days grown and used in many fields and it performed better than traditional computing in applications that need to be implemented by using mathematical algorithms. A mathematical algorithm requires more extensive and quicker calculations, higher computer architecture specification, and takes longer execution time. Therefore, there is a need to improve the use of computer hardware such as CPU, memory, etc. optimization has a main role to reduce the execution time and improve the utilization of computer recourses. And for the importance of execution time in implementing machine learning supervised module linear regression, in this paper we focus on optimizing machine learning algorithms, for this purpose we write a (Diabetes prediction program) and applying on it a Practical Swarm Optimization (PSO) to reduce the execution time and improve the utilization of computer resources. Finally, a massive improvement in execution time were observed.

• Korean Journal of Artificial Intelligence
• /
• v.11 no.1
• /
• pp.17-24
• /
• 2023

With the continuous growth in the amount of data collected and analyzed, deep learning has become increasingly popular for extracting meaningful insights from various fields. However, hardware limitations pose a challenge for achieving meaningful results with limited data. To address this challenge, this paper proposes an algorithm that leverages the characteristics of convolutional neural networks (CNNs) to reduce the size of image datasets by 20% through smoothing and shrinking the size of images using color elements. The proposed algorithm reduces the learning time and, as a result, the computational load on hardware. The experiments conducted in this study show that the proposed method achieves effective learning with similar or slightly higher accuracy than the original dataset while reducing computational and time costs. This color-centric dataset construction method using image smoothing techniques can lead to more efficient learning on CNNs. This method can be applied in various applications, such as image classification and recognition, and can contribute to more efficient and cost-effective deep learning. This paper presents a promising approach to reducing the computational load and time costs associated with deep learning and provides meaningful results with limited data, enabling them to apply deep learning to a broader range of applications.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.19 no.2
• /
• pp.79-87
• /
• 2023

On-line learning has been adopted as a major educational method due to the COVID-19 pandemic. Students and faculties got accustomed to on-line educational environment as they experienced it during the COVID-19 pandemic. Development of various technologies and social requirement for educational renovation lay groundwork for on-line learning as well. Therefore, on-line learning or blended learning will be likely to go on after the end of COVID-19 pandemic and it is necessary to prepare the guidelines for effective utilizing on-line learning. The primary purpose of this study is to examine the learning behaviors and the learning effects by using LMS data. Learning behaviors were measured in terms of learning time and access frequency for pre-recorded video lectures targeting computer-practice classes. The results of empirical analysis reveal that frequency was the significant predictor of course achievements but learning time was not. The findings of empirical analysis will provide insights that the effective planning and designing on-line classes based on learning behaviors are key to enhancing learning effects and learner's satisfaction.

• Journal of Educational Research in Mathematics
• /
• v.11 no.1
• /
• pp.51-66
• /
• 2001

In this paper, we tried to examine the didactical transpositions of the mathematical knowledges in the computer-based environment for mathematics learning and teaching, and also analyse the extreme didactical problems Computer has been regarded as an alterative that could overcome the difficulties in the teaching and learning of mathematics and many broad studies have been made to use computers in mathematics teaching and learning. But Any systematic analysis on the didactical problems of the computer-based environment for mathematics education has not been tried up to this time. In this paper, first of all, we analysed the didactical problems in the computer-based environment, and then, the mode of using computer for mathematics teaching and learning.

• Journal of Fisheries and Marine Sciences Education
• /
• v.15 no.2
• /
• pp.184-192
• /
• 2003

When the present Learning System for Computer-Related Subjects Using WBI is implemented on the Web with the above characteristics to help students to study computer subjects without any limitations of time or space, they can easily attain the goals of learning, have computer-utilizing abilities or information capacity, and enhance their capabilities for self-initiative learning. This system enables the learners to carry out 'plan-do-see' for the contents of learning initiatively. The learners can study the practice part of the curriculum using multi-media, such as motion pictures, voices, images, and sound effects, vividly with a sense of actual presence. It helps the students to have an active attitude toward leaning afterward. without meeting the teacher or without any storage media, the leaners can submit their assignments or materials for performance evaluation via the Internet.

• ETRI Journal
• /
• v.38 no.6
• /
• pp.1207-1217
• /
• 2016

The general method of machine learning has encountered disadvantages in terms of the significant amount of time and effort required for feature extraction and engineering in natural language processing. However, in recent years, these disadvantages have been solved using deep learning. In this paper, we propose a mention pair (MP) model using deep learning, and a system that combines both rule-based and deep learning-based systems using a guided MP as a coreference resolution, which is an information extraction technique. Our experiment results confirm that the proposed deep-learning based coreference resolution system achieves a better level of performance than rule- and statistics-based systems applied separately

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.6
• /
• pp.366-371
• /
• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.